Cluster Centric Technique for Association Rule Mining(C-ARM) For High Precision and Coverage in Web Page Recommendation
Manikandan R1, Saravanan V2
1Manikandan R , Research Scholar , Anna University, Chennai, India.
2Saravanan V , Professor and Dean, Department of Computer Applications,Sri venkateswara College of Computer Applications and Management, Coimbatore, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1732-1735 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8431088619/2019©BEIESP | DOI: 10.35940/ijeat.F8431.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Association Rule Mining (ARM) is known for its popularity and efficiency in the data mining domain. Over the recent years, the amount of data that gets accumulated in the internet is getting increased exponentially over time. The data available so are stored in online and are retrieved when a user requests for the same through key words with the help of a search engine. The important task of the search engines are to present the appropriate web pages that an user is expecting and in the modern times, The need of the hour is to recommend web pages to the users that he is interested in. This made the web page recommendation an important and vital task. Although many of the researchers are in the preliminary task of developing such systems, we in this research propose a recommendation model in which different users are interested upon a common item or domain by using the ARM concept. The data patterns that are in common are identified using the ARM and further these are clustered on a form of hierarchy .The clusters makes the recommendation system to easily identify the user group and based on the group, the pages are recommended, The experimental analysis are discussed and found to be efficient than the available methods in terms of computation time and reliability.
Keywords: Association Rule Mining (ARM) , Clustering, Data Mining, Web page recommendation , and User domains.